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# Verificamos la ruta donde se encuentra el archivo 
getwd()
## [1] "/home/alejo/Documentos/lab2"
datos<-read.csv("demandaBicis.csv", T, ",", encoding = "UTF-8")
head(datos)
##   instant     dteday season yr mnth hr holiday weekday workingday
## 1       1 2011-01-01      1  0    1  0       0       6          0
## 2       2 2011-01-01      1  0    1  1       0       6          0
## 3       3 2011-01-01      1  0    1  2       0       6          0
## 4       4 2011-01-01      1  0    1  3       0       6          0
## 5       5 2011-01-01      1  0    1  4       0       6          0
## 6       6 2011-01-01      1  0    1  5       0       6          0
##   weathersit temp  atemp  hum windspeed casual registered cnt
## 1          1 0.24 0.2879 0.81    0.0000      3         13  16
## 2          1 0.22 0.2727 0.80    0.0000      8         32  40
## 3          1 0.22 0.2727 0.80    0.0000      5         27  32
## 4          1 0.24 0.2879 0.75    0.0000      3         10  13
## 5          1 0.24 0.2879 0.75    0.0000      0          1   1
## 6          2 0.24 0.2576 0.75    0.0896      0          1   1

```

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Pregunta No. 1

¿Que mes es el que tiene la mayor demanda?

pregunta1<-datos%>%
  select(mnth,cnt)%>%
  group_by(mnth)%>%
  summarise(sumpreg1=sum(cnt))%>%
  arrange(desc(sumpreg1))
  head(pregunta1)
## # A tibble: 6 x 2
##    mnth sumpreg1
##   <int>    <int>
## 1     8   351194
## 2     6   346342
## 3     9   345991
## 4     7   344948
## 5     5   331686
## 6    10   322352

Respuesta: El mes con mayor demanda es agosto.

Pregunta No. 2 Que rango de hora es la de mayor demanda?

pregunta2<-datos%>%
  select(hr,cnt)%>%
  group_by(hr)%>%
  summarise(sumpreg2=sum(cnt))%>%
  arrange(desc(sumpreg2))
  head(pregunta2)
## # A tibble: 6 x 2
##      hr sumpreg2
##   <int>    <int>
## 1    17   336860
## 2    18   309772
## 3     8   261001
## 4    16   227748
## 5    19   226789
## 6    13   184919

La hora con mayor demanda es 17:00 horas.

Pregunta No. 3 Que temporada es la mas alta?

pregunta3<-datos%>%
  select(season,cnt)%>%
  group_by(season)%>%
  summarise(sumpreg3=sum(cnt))%>%
  arrange(desc(sumpreg3))
  head(pregunta3)
## # A tibble: 4 x 2
##   season sumpreg3
##    <int>    <int>
## 1      3  1061129
## 2      2   918589
## 3      4   841613
## 4      1   471348

La temporada mas alta es en otoño

Pregunta No. 4 A que temperatura baja la demanda?

pregunta4<-datos%>%
  select(temp,cnt)%>%
  group_by(temp)%>%
  summarise(sumpreg4=sum(cnt))%>%
  arrange(sumpreg4)
  head(pregunta4)
## # A tibble: 6 x 2
##    temp sumpreg4
##   <dbl>    <int>
## 1  1         294
## 2  0.08      480
## 3  0.98      539
## 4  0.04      570
## 5  0.06      672
## 6  0.02      712

La temperatura de menor demanda es 100 grados.

Pregunta No. 5 A que humedad baja la demanda?

pregunta5<-datos%>%
  select(hum,cnt)%>%
  group_by(hum)%>%
  summarise(sumpreg5=sum(cnt))%>%
  arrange(sumpreg5)
  head(pregunta5)
## # A tibble: 6 x 2
##     hum sumpreg5
##   <dbl>    <int>
## 1  0.13       17
## 2  0.12       29
## 3  0.14       38
## 4  0.97       64
## 5  0.08       77
## 6  0.1       107

La humedad de menor demanda es 13%. En temporadas secas y calurosas baja la demanda del servicio de bicicletas.

Pregunta No. 6 Condiciones ideales

c(pregunta1[1,1],pregunta2[1,1],pregunta3[1,1],pregunta4[6,1],pregunta5[6,1])
## $mnth
## [1] 8
## 
## $hr
## [1] 17
## 
## $season
## [1] 3
## 
## $temp
## [1] 0.02
## 
## $hum
## [1] 0.1

Pregunta No. 7 Grafica de la densidad de temperatura

library(plotly)
## Loading required package: ggplot2
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
pregunta7<-plot_ly(x=datos$temp,type = "histogram")
pregunta7

Pregunta No. 8 Con una gr?fica explique en que temporada hubieron mas rentas de bicicletas.

pregunta8<-plot_ly(x=pregunta3$season,y=pregunta3$sumpreg3,type = "bar")
pregunta8

Pregunta No. 9

preg9<-datos%>%
  select(season, cnt)
  head(preg9)
##   season cnt
## 1      1  16
## 2      1  40
## 3      1  32
## 4      1  13
## 5      1   1
## 6      1   1

Pregunta No. 10

preg10<-datos%>%
  select(weekday, casual, registered) %>%
  group_by(weekday)
  head(preg10)
## # A tibble: 6 x 3
## # Groups:   weekday [1]
##   weekday casual registered
##     <int>  <int>      <int>
## 1       6      3         13
## 2       6      8         32
## 3       6      5         27
## 4       6      3         10
## 5       6      0          1
## 6       6      0          1
pregunta10<-plot_ly(preg10, x = preg10$weekday, y = preg10$registered, type = 'bar', name = 'Registered') %>%
  add_trace(y = preg10$casual, name = 'Casual') %>%
  layout(yaxis = list(title = 'Count'), barmode = 'stack')
pregunta10